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Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning.

ABSTRACT: An outstanding challenge in the nascent field of materials informatics is to incorporate materials knowledge in a robust Bayesian approach to guide the discovery of new materials. Utilizing inputs from known phase diagrams, features or material descriptors that are known to affect the ferroelectric response, and Landau-Devonshire theory, we demonstrate our approach for BaTiO₃-based piezoelectrics with the desired target of a vertical morphotropic phase boundary. We predict, synthesize, and characterize a solid solution, (Ba_0.5Ca_0.5)TiO₃-Ba(Ti_0.7Zr_0.3)O₃, with piezoelectric properties that show better temperature reliability than other BaTiO₃-based piezoelectrics in our initial training data.

SUBMITTER: Xue D

PROVIDER: S-EPMC5127307 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning.

Xue Dezhen D Balachandran Prasanna V PV Yuan Ruihao R Hu Tao T Qian Xiaoning X Dougherty Edward R ER Lookman Turab T

Proceedings of the National Academy of Sciences of the United States of America 20161107 47

An outstanding challenge in the nascent field of materials informatics is to incorporate materials knowledge in a robust Bayesian approach to guide the discovery of new materials. Utilizing inputs from known phase diagrams, features or material descriptors that are known to affect the ferroelectric response, and Landau-Devonshire theory, we demonstrate our approach for BaTiO3-based piezoelectrics with the desired target of a vertical morphotropic phase boundary. We predict, synthesize ...[more]

PMID: 27821777

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